... text mining techniques, we extracted relevant features and applied a two-stage regression analysis alongside explainable machine learning to identify factors influencing suggestive opinion expression. Findings Females, individuals possessing more extensive social experience, and users who...

... of multiple emotions and mitigating biases in traditional usefulness voting methods. Design/methodology/approach This study employs a novel hybrid machine learning framework that integrates information entropy, principal component analysis (PCA) with K-means clustering and a bidirectional encoder...

... the detection of misinformation are determined. A series of machine learning (ML)-based and deep learning (DL)-based models are employed to assess whether comment environment features can help improve model performance. Finally, Shapley Additive Explanations (SHAP) are applied to interpret feature contributions...

... transparency issue. Since machine learning AI software all works by identifying patterns, its capabilities depend on the data it is trained on. Essentially, the software will try to replicate is training data. Thus, peer review software developed on one type of data may not work well on another, creating...

...Hossein Motahari-Nezhad Purpose No study has investigated the effects of different parameters on publication bias in meta-analyses using a machine learning approach. Therefore, this study aims to evaluate the impact of various factors on publication bias in meta-analyses. Design/methodology...

... Emerald Publishing Limited 2021 Emerald Publishing Limited Licensed re-use rights only Mobile search Information seeking behavior Cross-app behavior Machine learning User behavior Benefiting from the mobile internet development, mobile search engines have become an important way...